class_docstr: T5 model consisting of encoder and decoder transformers.
  
  This class simply wraps network.Transformer class in t5x.examples.t5.
  
  Attributes:
    vocab_size: Size of the vocabulary.
    emb_dim: Size of the embeddings.
    num_heads: Number of attention heads.
    num_encoder_layers: Number of encoder layers.
    num_decoder_layers: Number of decoder layers.
    head_dim: Size of the embeddings in each head.
    mlp_dim: Size of the MLP output embeddings.
    dropout_rate: Dropout rate.
    dtype: Data type.
    mlp_activations: Sequence of activations in MLP.
    logits_via_embedding: Use the embedding weights for computing logits.
code: def setup(self):
      self.t5_config = t5.T5Config(
          vocab_size=self.vocab_size,
          emb_dim=self.emb_dim,
          num_heads=self.num_heads,
          num_encoder_layers=self.num_encoder_layers,
          num_decoder_layers=self.num_decoder_layers,
          head_dim=self.head_dim,
          mlp_dim=self.mlp_dim,
          dropout_rate=self.dropout_rate,
          dtype=self.dtype,
          mlp_activations=self.mlp_activations,
          logits_via_embedding=self.logits_via_embedding,
          float32_attention_logits=self.float32_attention_logits,
      )
      self.t5_module = t5.Transformer(self.t5_config)
  
imports: import functools
  from typing import Any, Dict, Mapping, Optional, Sequence, Tuple
  import flax.linen
  import jax
  import jax.numpy
  from scenic.model_lib.base_models import base_model
  from t5x import decoding
  from t5x.examples.t5 import layers
  from t5x.examples.t5 import network
  from t5x.models import DecodeFnCallable
label_desc: Use image node_id 1 for calling the T5 obj's underlying member method code with example usage: obj.setup() without return types